Biology subjects

Moskon, M.

Publications and source records attributed to Moskon, M..

4 recordsLinked to original sources

bioRxiv2025

Stable reference genes for 24-hour circadian profiling of core clock genes in the blood of obstructive sleep apnea patients

Obstructive sleep apnea (OSA), one of the most common breathing disorders during sleep, is associated with circadian rhythm disruption, yet circadian genes expressed in blood remain largely unexplored as biomarkers. Reliable circadian gene expression analysis requires stable reference genes for qRT-PCR normalization, but no clear guidance exists for blood-based circadian studies in OSA. We evaluated the expression of 11 candidate reference genes (GAPDH, ACTB, RPL13A, PPIB, TBP, HPRT1, PPIA, SDHA, TUBB2A, UBC) in whole blood collected every 6 hours over 24 hours from 40 participants who underwent overnight respiratory polygraphy. Gene stability was assessed with RefFinder and EndoGeneAnalyzer across OSA severity levels, intra-individual variability, and time-point variability. ACTB and RPL13A consistently emerged as the most stable reference genes under all conditions. Their robustness was confirmed by personalized cosinor analysis of core clock genes (BMAL1, PER2, CRY1), showing that reliable normalization enables detection of circadian oscillations in clinical samples. While population-level analysis revealed no significant rhythmicity, individual profiles showed oscillations in 25 participants, independent of OSA severity. These findings identify ACTB and RPL13A as strong candidates for circadian transcriptomic studies in blood and provide a methodological foundation for biomarker discovery in sleep medicine, molecular medicine, and translational chronobiology.

molecular biology↗

bioRxiv2022

Identification and characterisation of variable oscillatory behaviour using CosinorPy

Background and objectiveAnalysis of rhythmic data has become an important aspect in biological and medical science, as well as other fields of science. Parametric methods based on trigonometric regression reflect several advantages in comparison to their alternatives. Software packages for parametric analysis of rhythmic data are mostly based on a single-component cosinor model, which is not able to describe asymmetric rhythms that might reflect multiple peaks per period. Moreover, a basic cosinor model is unable to describe rhythmicity that is changed with time. Here, we present some important extensions of the recently developed CosinorPy Python package to address these gaps. MethodsThe extended package CosinorPy provides the functionalities to perform a detailed individual or comparative analysis of rhythms reflecting (1) multiple asymmetric peaks per period, (2) forced, damped, or sustained rhythms, and (3) shift of the midline statistics of rhythm over time. In all these cases the package is able to assess the (differential) rhythmicity parameters, evaluate their significance and confidence intervals, and provide a set of publication-ready figures. ResultsWe demonstrate the package in some typical scenarios that incorporate different types of rhythmic dynamics, such as asymmetric, damped, and forced rhythms. We show that the proposed implementation of a generalised cosinor model is capable of reducing the error of estimated rhythmicity parameters in cases that tend to be problematic for alternative models. The implementation of the presented package is available together with scripts to reproduce the reported results at https://github.com/mmoskon/CosinorPy. ConclusionAccording to our knowledge, CosinorPy currently presents the only implementation that is able to cover all the above scenarios using a parametric model with vast applications in biology and medicine as well as in other scientific domains.

bioinformatics↗

bioRxiv2020

Common transcriptional programme of liver fibrosis in mouse genetic models and humans

Multifactorial metabolic diseases, such as non-alcoholic fatty liver disease, are a major burden of modern societies and frequently present with no clearly defined molecular biomarkers. Herein we used systems medicine approaches to decipher signatures of liver fibrosis in mouse models with malfunction in genes from unrelated biological pathways. Enrichment analyses of KEGG, Reactome and TRANSFAC databases complemented with genome-scale metabolic modelling revealed fibrotic signatures highly similar to liver pathologies in humans. The diverse genetic models of liver fibrosis exposed a common transcriptional programme with activated ER signalling, and a network of interactions between regulators of lipid metabolism and transcription factors from cancer pathways and immune system. The novel hallmarks of fibrosis are downregulated lipid pathways, including fatty acid, bile acid, and steroid hormone metabolism. Moreover, distinct metabolic subtypes of liver fibrosis were proposed, supported by unique enrichment of transcription factors based on the type of insult, disease stage, or sex.

molecular biology↗

bioRxiv2020

A Computational Design of a Programmable Biological Processor

Basic synthetic information processing structures, such as logic gates, oscillators and flip-flops, have already been implemented in living organisms. Current implementations of these structures are, however, hardly scalable and are yet to be extended to more complex processing structures that would constitute a biological computer. Herein, we make a step forward towards the construction of a biological computer. We describe a model-based computational design of a biological processor, composed of an instruction memory containing a biological program, a program counter that is used to address this memory and a biological oscillator that triggers the execution of the next instruction in the memory. The described processor uses transcription and translation resources of the host cell to perform its operations and is able to sequentially execute a set of instructions written within the so-called instruction memory implemented with non-volatile DNA sequences. The addressing of the instruction memory is achieved with a biological implementation of the Johnson counter, which increases its state after an instruction is executed. We additionally describe the implementation of a biological compiler that compiles a sequence of human-readable instructions into ordinary differential equations-based models. These models can be used to simulate the dynamics of the proposed processor. The proposed implementation presents the first programmable biological processor that exploits cellular resources to execute the specified instructions. We demonstrate the application of the proposed processor on a set of simple yet scalable biological programs. Biological descriptions of these programs can be written manually or can be generated automatically with the employment of the provided compiler.

synthetic biology↗